Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Field Service Excellence Leader, Regional

Johnson Controls
Manchester
1 year ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist (H/F)

Data Engineer | Hybrid | London | Databricks | Azure | 85k

Principal Data Scientist - Healthcare

Principal Data Scientist - Healthcare

Principal Data Scientist - Healthcare

Machine Learning Engineer (Databricks)

What you will do

As Field Service Excellence Leader, you will drive and support the continued development of field operations excellence for assigned regions/business units within the EMEALA Service business of $2B revenue and extended support to the systems/install business of $2.3B revenue. 

You’ll be responsible for building, maintaining and operation of modelling tools to create the business insights necessary to support a robust Integrated Business Planning (IBP) process with particular emphasis on demand planning and capacity planning for field operations.

You’ll action the change management plan to ensure alignment and action planning at different levels of the organization from operational level through to regional GM/VP level; optimise standardization of tools and self-serve functionality; drive regular cadence with stakeholders to ensure clear transparency and insights from IBP models and facilitate commercial and operational decision making and action planning to optimise alignment across demand forecasting, annual operating plans/targets and capacity planning.

You will ensure accountability is maintained with regional business leaders and support functions to deliver on corrective actions and execute an effective IBP process ensuring effective escalation where necessary and maintain region/sub region focus on productivity, COGS and contra rate to ensure positive absorption while optimizing competitive market positioning and drive corrective actions through regular cadence.

Identify and leverage benchmarking and best practices within EMEALA that support operational effectiveness and efficiency in driving profitable growth; lead and participate in continuous improvement initiatives to drive process and operational performance improvements, and leverage regional reviews to identify field engagement and L&D opportunities to develop high performing field service teams

How you will do it

Develop new and existing data and planning models to deliver actionable insights to operations teams Identify, source and translate large scale data to drive planning and performance models Integrate or otherwise leverage other KPI dashboards to provide holistic analysis and planning Teach, coach and consult with operational teams on self-serve models to ensure appropriate insights are drawn Engage GMs, commercial and operations leaders in IBP review cadence to build consensus and reconcile demand and capacity forecasts Maintain Rolling Actions and Issues/Decisions Log to manage accountability for identified actions Leverage subject matter expertise to provide guidance and recommendations to regional teams on options to bridge gaps versus plan and KPIs Regularly review performance across business units for benchmarking purposes and identify where best practice sharing and standardization opportunities exist Regularly review productivity/COGs/Absorption performance with regional teams and track and manage accountability for corrective actions Identify and execute continuous improvement opportunities both within the functional processes and with regional operations, leveraging structured improvement methods such as Lean/Six Sigma, machine learning and AI as appropriate Promote and leverage best practices across EMEALA and globally Collaborate with data scientists/analysts to define new/developing data requirements Produce ad hoc analysis and reporting to support field service excellence Conduct regular maintenance of data and models to ensure process efficiency, effectiveness and compliance

What we look for

Bachelor’s Degree from an accredited college or university and/or relevant depth of experience Solid understanding of field operations, resource planning methodologies and key financials with ability to identify and leverage relevant data points Advanced skills in use of analytic and visualisation tools (PowerBI in particular) Able to work effectively in a matrix organization structure and adept at influencing without authority Hands on approach to working in detail with data, balanced with business acumen to build relevant insights Data processing, model design and model-building phases (e.g., enterprise model, algorithm design, model refinement) on physical, distributed systems and cloud hosted environments Broad based experience with data modelling and planning methodologies with implementation across large data sets Strong analytical and process skills with a clear orientation towards actionable insights Good cultural awareness and ability to navigate a diverse international organisation Strong interpersonal skills with the ability to drive accountability for actions and results. Change management skills to ensure effective changes are not only identified and developed but are successfully deployed and adopted by business users Excellent communication and interpersonal skills, both internally and dealing with external stakeholders. Strong presentation skills with ability to support with the use effective visual aids Able to manage developmental, continuous improvement initiatives including cross functional project teams Lean / Six Sigma certified (preferred) Advanced knowledge of Microsoft Excel, Word and Power point software.

nd Power point software.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.